Many teams developing LLM-assisted tools skip verifying the model's correctness, focusing instead on fluency and coherence. This leads to tools that pass internal review because outputs "sound right" but fail in production because they lack verifiable accuracy against ground truth. This distinction becomes critical as LLM tools influence real business decisions, where "seems reasonable" is an insufficient standard.Qualitative evaluations, the standard approach, only catch obvious errors like poor formatting or off-topic responses. They consistently miss outputs that are subtly wrong, like confident but incorrect explanations, which can sound plausible but diverge significantly from actual facts. Such errors remain hidden without external validation.The alternative is an evaluation harness that scores model output against a labeled ground truth. The author built one for a root-cause explainer for data migration drift, revealing that even fluent initial prototypes were often factually incorrect. This harness comprised three parts for accurate assessment.First, a synthetic ground truth dataset of known correct answers established by design. These scenarios required careful construction to be realistic, including noise and overlapping signals, to accurately predict real-world performance. Second, a scoring function that evaluated ranked outputs based on the presence and rank of the correct answer, moving beyond simple binary correctness. Third, systematic evaluation across the entire dataset, rather than spot-checking, to reveal overall patterns of reliability or consistent error.This systematic evaluation revealed that schema change scenarios were reliably handled, but transformation logic bugs often led to misattribution. Critically, overlapping-signal scenarios produced the highest rate of confidently wrong explanations, a finding qualitative review would never have surfaced. The model's expressed confidence did not correlate with its accuracy.For enterprise AI deployment, especially for tools influencing critical decisions, teams must measure accuracy against known correct answers, not just perceived reasonableness. Building a synthetic ground truth dataset is the most challenging but crucial step, compelling a precise definition of "correct" for the specific use case. Without this, organizations risk deploying tools that are fluent but fundamentally incorrect, undermining their value.